Learning to Rank for Information Retrieval
Due to the fast growth of the Web and the difficulties in finding desired information, efficient and effective information retrieval systems have become more important than ever, and the search engine has become an essential tool for many people. The rank
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Tie-Yan Liu
Learning to Rank for Information Retrieval
Tie-Yan Liu Microsoft Research Asia Bldg #2, No. 5, Dan Ling Street Haidian District Beijing 100080 People’s Republic of China [email protected]
ISBN 978-3-642-14266-6 e-ISBN 978-3-642-14267-3 DOI 10.1007/978-3-642-14267-3 Springer Heidelberg Dordrecht London New York Library of Congress Control Number: 2011927168 © Springer-Verlag Berlin Heidelberg 2011 This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilm or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer. Violations are liable to prosecution under the German Copyright Law. The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. Cover design: KünkelLopka GmbH Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)
Preface
In recent years, with the fast growth of the World Wide Web and the difficulties in finding desired information, efficient and effective information retrieval systems have become more important than ever, and the search engine has become an essential tool for many people. The ranker, a central component in every search engine, is responsible for the matching between processed queries and indexed documents. Because of its central role, great attention has been paid to the research and development of ranking technologies. In addition, ranking is also pivotal for many other information retrieval applications, such as collaborative filtering, question answering, multimedia retrieval, text summarization, and online advertising. Leveraging machine learning technologies in the ranking process has led to innovative and more effective ranking models, and has also led to the emerging of a new research area named learning to rank. This new book gives a comprehensive review of the major approaches to learning to rank, i.e., the pointwise, pairwise, and listwise approaches. For each approach, the basic framework, example algorithms, and their theoretical properties are discussed. Then some recent advances in learning to rank that are orthogonal to the three major approaches are introduced, including relational ranking, query-dependent ranking, semi-supervised ranking, and transfer ranking. Next, we introduce the benchmark datasets for the research on learning to rank and discuss some practical issues regarding the application of learning to rank, such as click-through log mining and training data selection/preprocessing. After that severa
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